abstract
Adsorption isotherms obtained through volumetric measurements are widely used to estimate the gas adsorption performance of porous materials. Nonetheless, there is always ambiguity regarding the contributions of chemi- and physisorption processes to the overall retained gas volume. In this work, we propose, for the first time, the use of solid-state NMR (ssNMR) to generate isotherms of CO2 adsorbed onto an amine-modified silica sorbent. This method enables the separation of six individual isotherms for chemi- and physisorbed CO2 components, a feat only possible using the discrimination power of NMR spectroscopy. The adsorption mechanism for each adsorbed species was ascertained by tracking their adsorption profiles at various pressures. The proposed method was validated against conventional volumetric adsorption measurements. The isotherm curves obtained by the proposed ssNMR-assisted approach enable advanced analysis of the sorbents, revealing the potential of variable-pressure NMR experiments in gas adsorption applications.
keywords
CARBON-DIOXIDE; PRESSURE; SILICA
subject category
Chemistry
authors
Ilkaeva, M; Vieira, R; Pereira, JMP; Sardo, M; Marin-Montesinos, I; Mafra, L
our authors
Projects
CICECO - Aveiro Institute of Materials (UIDB/50011/2020)
CICECO - Aveiro Institute of Materials (UIDP/50011/2020)
Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)
Other
Cover
Mediaacknowledgements
This work was developed wit h i n the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020, and LA/P/0006/2020, financed by na- tional funds through the FCT/MEC (PIDDAC) . We also acknowledge funding from project PTDC/QUI-QFI/28747/2017 (GAS2MAT-DNPSENS-POCI-01-0145-FEDER-028) , financed through FCT/MEC and cofinanced by FEDER under the PT2020 Partnership Agreement. The NMR spectrometers are part of the National NMR Network (PTNMR) and are partially supported by Infrastructure Project 022161 (cofinanced by FEDER through COMPETE 2020, POCI and PORL, and FCT through PIDDAC) . This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement 865974) . FCT is also acknowledged by R.V., M.I., and M.S. for Researcher Positions (CEECIND/02127/2017, CEECIND/00546/2018, and CEECIND/00056/2020, respectively) and by J.P. for a Ph.D. Studentship (SFRH/BD/145004/2019) .